Overview

Dataset statistics

Number of variables27
Number of observations11703
Missing cells62929
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 MiB
Average record size in memory1.1 KiB

Variable types

CAT17
NUM9
BOOL1

Warnings

Application Type has constant value "11703" Constant
Building Area has constant value "11703" Constant
Added Date has a high cardinality: 3798 distinct values High cardinality
Issue Date has a high cardinality: 3972 distinct values High cardinality
Final Date has a high cardinality: 2816 distinct values High cardinality
Expired Date has a high cardinality: 70 distinct values High cardinality
Description of Work has a high cardinality: 7368 distinct values High cardinality
Street Number has a high cardinality: 5220 distinct values High cardinality
Street Name has a high cardinality: 907 distinct values High cardinality
General Location has a high cardinality: 549 distinct values High cardinality
Location has a high cardinality: 6155 distinct values High cardinality
Longitude is highly correlated with LatitudeHigh correlation
Latitude is highly correlated with Longitude and 1 other fieldsHigh correlation
Councils is highly correlated with Council DistrictsHigh correlation
Council Districts is highly correlated with CouncilsHigh correlation
Communities is highly correlated with LatitudeHigh correlation
Final Date has 4636 (39.6%) missing values Missing
Expired Date has 11622 (99.3%) missing values Missing
Description of Work has 2435 (20.8%) missing values Missing
Pre-direction has 11444 (97.8%) missing values Missing
Post-direction has 11689 (99.9%) missing values Missing
General Location has 10937 (93.5%) missing values Missing
Council Districts has 1985 (17.0%) missing values Missing
Councils has 1985 (17.0%) missing values Missing
Communities has 2008 (17.2%) missing values Missing
Zip Codes has 1985 (17.0%) missing values Missing
Municipalities has 1985 (17.0%) missing values Missing
Zip Code is highly skewed (γ1 = -74.45075304) Skewed
Expired Date is uniformly distributed Uniform
Permit Number has unique values Unique
Latitude has 1985 (17.0%) zeros Zeros
Longitude has 1985 (17.0%) zeros Zeros

Reproduction

Analysis started2020-12-12 20:50:53.605686
Analysis finished2020-12-12 20:51:03.716887
Duration10.11 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Application Type
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
USE & OCCUPANCY PERMIT
11703 
ValueCountFrequency (%) 
USE & OCCUPANCY PERMIT11703100.0%
 
2020-12-12T15:51:03.770934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:03.811969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:03.854005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3510913.6%
 
C3510913.6%
 
U234069.1%
 
E234069.1%
 
P234069.1%
 
S117034.5%
 
&117034.5%
 
O117034.5%
 
A117034.5%
 
N117034.5%
 
Y117034.5%
 
R117034.5%
 
M117034.5%
 
I117034.5%
 
T117034.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter21065481.8%
 
Space Separator3510913.6%
 
Other Punctuation117034.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C3510916.7%
 
U2340611.1%
 
E2340611.1%
 
P2340611.1%
 
S117035.6%
 
O117035.6%
 
A117035.6%
 
N117035.6%
 
Y117035.6%
 
R117035.6%
 
M117035.6%
 
I117035.6%
 
T117035.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
35109100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
&11703100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin21065481.8%
 
Common4681218.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C3510916.7%
 
U2340611.1%
 
E2340611.1%
 
P2340611.1%
 
S117035.6%
 
O117035.6%
 
A117035.6%
 
N117035.6%
 
Y117035.6%
 
R117035.6%
 
M117035.6%
 
I117035.6%
 
T117035.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
3510975.0%
 
&1170325.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII257466100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3510913.6%
 
C3510913.6%
 
U234069.1%
 
E234069.1%
 
P234069.1%
 
S117034.5%
 
&117034.5%
 
O117034.5%
 
A117034.5%
 
N117034.5%
 
Y117034.5%
 
R117034.5%
 
M117034.5%
 
I117034.5%
 
T117034.5%
 

Permit Number
Real number (ℝ≥0)

UNIQUE

Distinct11703
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277957.1314
Minimum200419
Maximum2017540
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:03.929070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum200419
5-th percentile203178.6
Q1225967.5
median264491
Q3345242
95-th percentile366492.9
Maximum2017540
Range1817121
Interquartile range (IQR)119274.5

Descriptive statistics

Standard deviation59414.58618
Coefficient of variation (CV)0.2137544947
Kurtosis61.25942703
Mean277957.1314
Median Absolute Deviation (MAD)46955
Skewness2.450342841
Sum3252932309
Variance3530093050
MonotocityNot monotonic
2020-12-12T15:51:04.013142image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3461111< 0.1%
 
3576541< 0.1%
 
3461161< 0.1%
 
2593641< 0.1%
 
2962261< 0.1%
 
2593561< 0.1%
 
2634501< 0.1%
 
2347761< 0.1%
 
2736871< 0.1%
 
2552541< 0.1%
 
2447671< 0.1%
 
3412681< 0.1%
 
2654911< 0.1%
 
2183841< 0.1%
 
2736791< 0.1%
 
2880141< 0.1%
 
2061761< 0.1%
 
3521691< 0.1%
 
3125861< 0.1%
 
3514971< 0.1%
 
3658321< 0.1%
 
3208061< 0.1%
 
2049731< 0.1%
 
2613811< 0.1%
 
3525301< 0.1%
 
Other values (11678)1167899.8%
 
ValueCountFrequency (%) 
2004191< 0.1%
 
2004201< 0.1%
 
2004251< 0.1%
 
2004331< 0.1%
 
2004341< 0.1%
 
2004461< 0.1%
 
2004471< 0.1%
 
2004741< 0.1%
 
2004821< 0.1%
 
2004841< 0.1%
 
ValueCountFrequency (%) 
20175401< 0.1%
 
3751831< 0.1%
 
3751601< 0.1%
 
3750881< 0.1%
 
3750351< 0.1%
 
3748531< 0.1%
 
3748251< 0.1%
 
3746131< 0.1%
 
3744251< 0.1%
 
3743551< 0.1%
 

Work Type
Categorical

Distinct6
Distinct (%)0.1%
Missing59
Missing (%)0.5%
Memory size91.6 KiB
OCCUPY
9529 
USE
1798 
ALTER
 
167
CONST
 
104
INSTAL
 
34
ValueCountFrequency (%) 
OCCUPY952981.4%
 
USE179815.4%
 
ALTER1671.4%
 
CONST1040.9%
 
INSTAL340.3%
 
ADD120.1%
 
(Missing)590.5%
 
2020-12-12T15:51:04.097215image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:04.144755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:04.203806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.497735623
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
C1916229.8%
 
U1132717.6%
 
O963315.0%
 
P952914.8%
 
Y952914.8%
 
E19653.1%
 
S19363.0%
 
T3050.5%
 
A2130.3%
 
L2010.3%
 
R1670.3%
 
N1380.2%
 
n1180.2%
 
a590.1%
 
I340.1%
 
D24< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter6416399.7%
 
Lowercase Letter1770.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1916229.9%
 
U1132717.7%
 
O963315.0%
 
P952914.9%
 
Y952914.9%
 
E19653.1%
 
S19363.0%
 
T3050.5%
 
A2130.3%
 
L2010.3%
 
R1670.3%
 
N1380.2%
 
I340.1%
 
D24< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n11866.7%
 
a5933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin64340100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
C1916229.8%
 
U1132717.6%
 
O963315.0%
 
P952914.8%
 
Y952914.8%
 
E19653.1%
 
S19363.0%
 
T3050.5%
 
A2130.3%
 
L2010.3%
 
R1670.3%
 
N1380.2%
 
n1180.2%
 
a590.1%
 
I340.1%
 
D24< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII64340100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
C1916229.8%
 
U1132717.6%
 
O963315.0%
 
P952914.8%
 
Y952914.8%
 
E19653.1%
 
S19363.0%
 
T3050.5%
 
A2130.3%
 
L2010.3%
 
R1670.3%
 
N1380.2%
 
n1180.2%
 
a590.1%
 
I340.1%
 
D24< 0.1%
 

Added Date
Categorical

HIGH CARDINALITY

Distinct3798
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
02/29/2004
 
85
11/30/1999
 
59
03/27/2019
 
51
07/07/2011
 
49
07/13/2011
 
47
Other values (3793)
11412 
ValueCountFrequency (%) 
02/29/2004850.7%
 
11/30/1999590.5%
 
03/27/2019510.4%
 
07/07/2011490.4%
 
07/13/2011470.4%
 
11/23/1999440.4%
 
02/11/2019420.4%
 
12/01/1999410.4%
 
06/17/2003400.3%
 
01/07/2000370.3%
 
05/02/2018360.3%
 
07/15/2002360.3%
 
03/17/2004360.3%
 
11/23/2007350.3%
 
06/26/2002350.3%
 
03/10/2015350.3%
 
05/11/2001330.3%
 
04/20/2009320.3%
 
05/01/2018320.3%
 
10/07/2015320.3%
 
08/16/2017300.3%
 
07/02/2009300.3%
 
06/07/2003290.2%
 
03/25/2014290.2%
 
06/27/2011280.2%
 
Other values (3773)1072091.6%
 
2020-12-12T15:51:04.286878image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1570 ?
Unique (%)13.4%
2020-12-12T15:51:04.357939image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
03230627.6%
 
/2340620.0%
 
21945316.6%
 
11751915.0%
 
944083.8%
 
337183.2%
 
733782.9%
 
633622.9%
 
532522.8%
 
431372.7%
 
830912.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9362480.0%
 
Other Punctuation2340620.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03230634.5%
 
21945320.8%
 
11751918.7%
 
944084.7%
 
337184.0%
 
733783.6%
 
633623.6%
 
532523.5%
 
431373.4%
 
830913.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/23406100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common117030100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
03230627.6%
 
/2340620.0%
 
21945316.6%
 
11751915.0%
 
944083.8%
 
337183.2%
 
733782.9%
 
633622.9%
 
532522.8%
 
431372.7%
 
830912.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII117030100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
03230627.6%
 
/2340620.0%
 
21945316.6%
 
11751915.0%
 
944083.8%
 
337183.2%
 
733782.9%
 
633622.9%
 
532522.8%
 
431372.7%
 
830912.6%
 

Issue Date
Categorical

HIGH CARDINALITY

Distinct3972
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
04/22/2019
 
47
10/26/2000
 
34
01/15/2019
 
25
02/09/2006
 
24
04/10/2000
 
23
Other values (3967)
11550 
ValueCountFrequency (%) 
04/22/2019470.4%
 
10/26/2000340.3%
 
01/15/2019250.2%
 
02/09/2006240.2%
 
04/10/2000230.2%
 
09/26/2013210.2%
 
09/23/2004210.2%
 
05/13/2002210.2%
 
12/11/2016200.2%
 
05/17/2000200.2%
 
01/31/2007200.2%
 
04/17/2002190.2%
 
05/16/2000180.2%
 
04/27/2000180.2%
 
04/18/2019180.2%
 
03/24/2009180.2%
 
12/09/2016180.2%
 
11/13/2013180.2%
 
09/14/2017180.2%
 
05/14/2002170.1%
 
02/17/2009170.1%
 
11/16/2016160.1%
 
07/16/2005160.1%
 
07/20/2000160.1%
 
03/05/2014150.1%
 
Other values (3947)1118595.6%
 
2020-12-12T15:51:04.440009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1330 ?
Unique (%)11.4%
2020-12-12T15:51:04.511071image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
03261827.9%
 
/2340620.0%
 
22005917.1%
 
11734714.8%
 
338873.3%
 
935013.0%
 
634482.9%
 
433232.8%
 
532292.8%
 
831902.7%
 
730222.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number9362480.0%
 
Other Punctuation2340620.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03261834.8%
 
22005921.4%
 
11734718.5%
 
338874.2%
 
935013.7%
 
634483.7%
 
433233.5%
 
532293.4%
 
831903.4%
 
730223.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/23406100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common117030100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
03261827.9%
 
/2340620.0%
 
22005917.1%
 
11734714.8%
 
338873.3%
 
935013.0%
 
634482.9%
 
433232.8%
 
532292.8%
 
831902.7%
 
730222.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII117030100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
03261827.9%
 
/2340620.0%
 
22005917.1%
 
11734714.8%
 
338873.3%
 
935013.0%
 
634482.9%
 
433232.8%
 
532292.8%
 
831902.7%
 
730222.6%
 

Final Date
Categorical

HIGH CARDINALITY
MISSING

Distinct2816
Distinct (%)39.8%
Missing4636
Missing (%)39.6%
Memory size91.6 KiB
04/15/2013
 
39
01/05/2015
 
25
01/15/2019
 
24
09/26/2013
 
22
12/11/2016
 
20
Other values (2811)
6937 
ValueCountFrequency (%) 
04/15/2013390.3%
 
01/05/2015250.2%
 
01/15/2019240.2%
 
09/26/2013220.2%
 
12/11/2016200.2%
 
12/09/2016180.2%
 
09/14/2017180.2%
 
11/13/2013170.1%
 
04/22/2019170.1%
 
11/16/2016160.1%
 
04/04/2019150.1%
 
10/04/2013150.1%
 
03/05/2014150.1%
 
05/13/2009150.1%
 
10/29/2014140.1%
 
05/23/2018140.1%
 
10/11/2016140.1%
 
07/30/2019140.1%
 
04/24/2013130.1%
 
03/31/2014130.1%
 
11/08/2016130.1%
 
08/17/2017130.1%
 
10/29/2013120.1%
 
09/27/2019120.1%
 
09/03/2009120.1%
 
Other values (2791)664756.8%
 
(Missing)463639.6%
 
2020-12-12T15:51:04.595643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1228 ?
Unique (%)17.4%
2020-12-12T15:51:04.676713image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length7.227035803
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01745620.6%
 
/1413416.7%
 
11204514.2%
 
21194414.1%
 
n927211.0%
 
a46365.5%
 
324522.9%
 
923822.8%
 
621542.5%
 
820942.5%
 
520532.4%
 
420512.4%
 
719052.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5653666.8%
 
Other Punctuation1413416.7%
 
Lowercase Letter1390816.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01745630.9%
 
11204521.3%
 
21194421.1%
 
324524.3%
 
923824.2%
 
621543.8%
 
820943.7%
 
520533.6%
 
420513.6%
 
719053.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/14134100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n927266.7%
 
a463633.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common7067083.6%
 
Latin1390816.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
01745624.7%
 
/1413420.0%
 
11204517.0%
 
21194416.9%
 
324523.5%
 
923823.4%
 
621543.0%
 
820943.0%
 
520532.9%
 
420512.9%
 
719052.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n927266.7%
 
a463633.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII84578100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01745620.6%
 
/1413416.7%
 
11204514.2%
 
21194414.1%
 
n927211.0%
 
a46365.5%
 
324522.9%
 
923822.8%
 
621542.5%
 
820942.5%
 
520532.4%
 
420512.4%
 
719052.3%
 

Expired Date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct70
Distinct (%)86.4%
Missing11622
Missing (%)99.3%
Memory size91.6 KiB
10/30/2008
 
4
11/06/2009
 
4
11/24/2009
 
2
12/25/2017
 
2
11/21/2008
 
2
Other values (65)
67 
ValueCountFrequency (%) 
10/30/20084< 0.1%
 
11/06/20094< 0.1%
 
11/24/20092< 0.1%
 
12/25/20172< 0.1%
 
11/21/20082< 0.1%
 
06/13/20052< 0.1%
 
12/30/20112< 0.1%
 
10/01/20141< 0.1%
 
11/08/20181< 0.1%
 
01/10/20091< 0.1%
 
07/05/20091< 0.1%
 
08/31/20081< 0.1%
 
07/15/20121< 0.1%
 
06/29/20101< 0.1%
 
10/21/20111< 0.1%
 
06/01/20121< 0.1%
 
12/25/20071< 0.1%
 
09/05/20111< 0.1%
 
01/08/20121< 0.1%
 
10/15/20111< 0.1%
 
12/25/20061< 0.1%
 
06/15/20051< 0.1%
 
11/05/20071< 0.1%
 
05/04/20201< 0.1%
 
03/21/20091< 0.1%
 
Other values (45)450.4%
 
(Missing)1162299.3%
 
2020-12-12T15:51:04.761286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique63 ?
Unique (%)77.8%
2020-12-12T15:51:04.839854image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.048449116
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2324465.2%
 
a1162232.6%
 
02160.6%
 
/1620.5%
 
21490.4%
 
11440.4%
 
8250.1%
 
3230.1%
 
9220.1%
 
6220.1%
 
5220.1%
 
714< 0.1%
 
411< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3486697.7%
 
Decimal Number6481.8%
 
Other Punctuation1620.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2324466.7%
 
a1162233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
021633.3%
 
214923.0%
 
114422.2%
 
8253.9%
 
3233.5%
 
9223.4%
 
6223.4%
 
5223.4%
 
7142.2%
 
4111.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/162100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3486697.7%
 
Common8102.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2324466.7%
 
a1162233.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
021626.7%
 
/16220.0%
 
214918.4%
 
114417.8%
 
8253.1%
 
3232.8%
 
9222.7%
 
6222.7%
 
5222.7%
 
7141.7%
 
4111.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII35676100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2324465.2%
 
a1162232.6%
 
02160.6%
 
/1620.5%
 
21490.4%
 
11440.4%
 
8250.1%
 
3230.1%
 
9220.1%
 
6220.1%
 
5220.1%
 
714< 0.1%
 
411< 0.1%
 

Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
Finaled
7056 
Issued
4613 
Stop Work
 
34
ValueCountFrequency (%) 
Finaled705660.3%
 
Issued461339.4%
 
Stop Work340.3%
 
2020-12-12T15:51:04.908413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:04.952451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:05.003995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length7
Mean length6.611638042
Min length6

Overview of Unicode Properties

Unique unicode characters18
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1166915.1%
 
d1166915.1%
 
s922611.9%
 
F70569.1%
 
i70569.1%
 
n70569.1%
 
a70569.1%
 
l70569.1%
 
I46136.0%
 
u46136.0%
 
o680.1%
 
S34< 0.1%
 
t34< 0.1%
 
p34< 0.1%
 
34< 0.1%
 
W34< 0.1%
 
r34< 0.1%
 
k34< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6560584.8%
 
Uppercase Letter1173715.2%
 
Space Separator34< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F705660.1%
 
I461339.3%
 
S340.3%
 
W340.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1166917.8%
 
d1166917.8%
 
s922614.1%
 
i705610.8%
 
n705610.8%
 
a705610.8%
 
l705610.8%
 
u46137.0%
 
o680.1%
 
t340.1%
 
p340.1%
 
r340.1%
 
k340.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
34100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin77342> 99.9%
 
Common34< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1166915.1%
 
d1166915.1%
 
s922611.9%
 
F70569.1%
 
i70569.1%
 
n70569.1%
 
a70569.1%
 
l70569.1%
 
I46136.0%
 
u46136.0%
 
o680.1%
 
S34< 0.1%
 
t34< 0.1%
 
p34< 0.1%
 
W34< 0.1%
 
r34< 0.1%
 
k34< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
34100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII77376100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1166915.1%
 
d1166915.1%
 
s922611.9%
 
F70569.1%
 
i70569.1%
 
n70569.1%
 
a70569.1%
 
l70569.1%
 
I46136.0%
 
u46136.0%
 
o680.1%
 
S34< 0.1%
 
t34< 0.1%
 
p34< 0.1%
 
34< 0.1%
 
W34< 0.1%
 
r34< 0.1%
 
k34< 0.1%
 

Building Area
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
1
11703 
ValueCountFrequency (%) 
111703100.0%
 
2020-12-12T15:51:05.049534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Description of Work
Categorical

HIGH CARDINALITY
MISSING

Distinct7368
Distinct (%)79.5%
Missing2435
Missing (%)20.8%
Memory size91.6 KiB
4 STORY TOWNHOUSE
 
191
NEW TOWNHOME
 
111
TOWNHOME
 
91
Green Tape
 
86
TWO OVER TWO CONDO
 
39
Other values (7363)
8750 
ValueCountFrequency (%) 
4 STORY TOWNHOUSE1911.6%
 
NEW TOWNHOME1110.9%
 
TOWNHOME910.8%
 
Green Tape860.7%
 
TWO OVER TWO CONDO390.3%
 
New T/H320.3%
 
MFD300.3%
 
NEW T/H300.3%
 
PiggyBack Townhouse290.2%
 
4 LEVEL TOWNHOUSE250.2%
 
IBC TOWNHOUSE240.2%
 
Unit A240.2%
 
Existing Use: N/A (New Construction) Proposed Use: Multi-family Apartments Haz Mat: No Pri Use: Multi-family Dwelling % of Space: 100 Ready for Inspection: No240.2%
 
Unit B240.2%
 
Construction Trailer220.2%
 
vacant lot - NEW MFD210.2%
 
MATISSE MODEL - 2 OVER 2 - MPDU210.2%
 
RESTAURANT200.2%
 
NEW MFD180.2%
 
4 Story Commercial Townhouses180.2%
 
4 STORY TOWNHOME170.1%
 
MPDU CONDO160.1%
 
MPDU Condo160.1%
 
CHANGE OF USE150.1%
 
CONDO UNIT150.1%
 
Other values (7343)830971.0%
 
(Missing)243520.8%
 
2020-12-12T15:51:05.122096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6982 ?
Unique (%)75.3%
2020-12-12T15:51:05.218179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1117
Median length23
Mean length48.68768692
Min length1

Overview of Unicode Properties

Unique unicode characters93
Unique unicode categories13 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
7981714.0%
 
e312915.5%
 
o223783.9%
 
n217943.8%
 
s211573.7%
 
a211143.7%
 
i175183.1%
 
t160082.8%
 
r157232.8%
 
E147712.6%
 
S125642.2%
 
N114352.0%
 
p114242.0%
 
O109541.9%
 
:108911.9%
 
T105791.9%
 
l102391.8%
 
A96981.7%
 
R96641.7%
 
96261.7%
 
u84051.5%
 
U82801.5%
 
I82461.4%
 
f80491.4%
 
079571.4%
 
Other values (68)16021028.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter25332444.5%
 
Uppercase Letter14838426.0%
 
Space Separator7981714.0%
 
Decimal Number389816.8%
 
Other Punctuation319495.6%
 
Control96291.7%
 
Dash Punctuation40530.7%
 
Open Punctuation15750.3%
 
Close Punctuation15620.3%
 
Math Symbol5050.1%
 
Currency Symbol10< 0.1%
 
Connector Punctuation2< 0.1%
 
Other Symbol1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0795720.4%
 
1684217.6%
 
2508813.1%
 
332098.2%
 
428967.4%
 
628437.3%
 
528387.3%
 
825646.6%
 
723766.1%
 
923686.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E1477110.0%
 
S125648.5%
 
N114357.7%
 
O109547.4%
 
T105797.1%
 
A96986.5%
 
R96646.5%
 
U82805.6%
 
I82465.6%
 
C65134.4%
 
P62384.2%
 
L57973.9%
 
M53033.6%
 
F50923.4%
 
H46213.1%
 
D43152.9%
 
B41362.8%
 
G31402.1%
 
Y19841.3%
 
W19801.3%
 
V17301.2%
 
K5880.4%
 
X2570.2%
 
J2170.1%
 
Z1580.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
79817100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:1089134.1%
 
;405212.7%
 
&394812.4%
 
/30009.4%
 
#29949.4%
 
.23637.4%
 
%22847.1%
 
,17355.4%
 
*2900.9%
 
'1940.6%
 
"1340.4%
 
@420.1%
 
?200.1%
 
!2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3129112.4%
 
o223788.8%
 
n217948.6%
 
s211578.4%
 
a211148.3%
 
i175186.9%
 
t160086.3%
 
r157236.2%
 
p114244.5%
 
l102394.0%
 
u84053.3%
 
f80493.2%
 
c78943.1%
 
d75793.0%
 
m63452.5%
 
g53152.1%
 
b46481.8%
 
h44041.7%
 
y42531.7%
 
w20430.8%
 
q14710.6%
 
x14530.6%
 
z10890.4%
 
v9910.4%
 
k6900.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4053100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(157399.9%
 
[20.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)156199.9%
 
]10.1%
 

Most frequent Control characters

ValueCountFrequency (%) 
9626> 99.9%
 
2< 0.1%
 
1< 0.1%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
=48696.2%
 
+122.4%
 
<40.8%
 
~20.4%
 
>10.2%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$10100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_2100.0%
 

Most frequent Other Symbol characters

ValueCountFrequency (%) 
1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin40170870.5%
 
Common16808429.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
7981747.5%
 
:108916.5%
 
96265.7%
 
079574.7%
 
168424.1%
 
250883.0%
 
-40532.4%
 
;40522.4%
 
&39482.3%
 
332091.9%
 
/30001.8%
 
#29941.8%
 
428961.7%
 
628431.7%
 
528381.7%
 
825641.5%
 
723761.4%
 
923681.4%
 
.23631.4%
 
%22841.4%
 
,17351.0%
 
(15730.9%
 
)15610.9%
 
=4860.3%
 
*2900.2%
 
Other values (16)4300.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e312917.8%
 
o223785.6%
 
n217945.4%
 
s211575.3%
 
a211145.3%
 
i175184.4%
 
t160084.0%
 
r157233.9%
 
E147713.7%
 
S125643.1%
 
N114352.8%
 
p114242.8%
 
O109542.7%
 
T105792.6%
 
l102392.5%
 
A96982.4%
 
R96642.4%
 
u84052.1%
 
U82802.1%
 
I82462.1%
 
f80492.0%
 
c78942.0%
 
d75791.9%
 
C65131.6%
 
m63451.6%
 
Other values (27)7208617.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII569791> 99.9%
 
Specials1< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
7981714.0%
 
e312915.5%
 
o223783.9%
 
n217943.8%
 
s211573.7%
 
a211143.7%
 
i175183.1%
 
t160082.8%
 
r157232.8%
 
E147712.6%
 
S125642.2%
 
N114352.0%
 
p114242.0%
 
O109541.9%
 
:108911.9%
 
T105791.9%
 
l102391.8%
 
A96981.7%
 
R96641.7%
 
96261.7%
 
u84051.5%
 
U82801.5%
 
I82461.4%
 
f80491.4%
 
079571.4%
 
Other values (67)16020928.1%
 

Most frequent Specials characters

ValueCountFrequency (%) 
1100.0%
 

Street Number
Categorical

HIGH CARDINALITY

Distinct5220
Distinct (%)44.7%
Missing25
Missing (%)0.2%
Memory size91.6 KiB
11160
 
171
7101
 
150
22705
 
104
8250
 
47
7501
 
46
Other values (5215)
11160 
ValueCountFrequency (%) 
111601711.5%
 
71011501.3%
 
227051040.9%
 
8250470.4%
 
7501460.4%
 
1450.4%
 
5600420.4%
 
8300410.4%
 
5230390.3%
 
1155360.3%
 
8661350.3%
 
4800340.3%
 
11215310.3%
 
930310.3%
 
8010310.3%
 
2425290.2%
 
10000280.2%
 
11700260.2%
 
8621260.2%
 
11855260.2%
 
1200250.2%
 
1133250.2%
 
11601250.2%
 
10400240.2%
 
4960240.2%
 
Other values (5195)1053790.0%
 
(Missing)250.2%
 
2020-12-12T15:51:05.320767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3240 ?
Unique (%)27.7%
2020-12-12T15:51:05.402338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length4.41920875
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11190223.0%
 
0859816.6%
 
2660412.8%
 
543318.4%
 
341158.0%
 
434546.7%
 
832246.2%
 
731916.2%
 
631406.1%
 
930705.9%
 
n500.1%
 
a25< 0.1%
 
A10< 0.1%
 
-3< 0.1%
 
C1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number5162999.8%
 
Lowercase Letter750.1%
 
Uppercase Letter11< 0.1%
 
Dash Punctuation3< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11190223.1%
 
0859816.7%
 
2660412.8%
 
543318.4%
 
341158.0%
 
434546.7%
 
832246.2%
 
731916.2%
 
631406.1%
 
930705.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n5066.7%
 
a2533.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-3100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1090.9%
 
C19.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common5163299.8%
 
Latin860.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
11190223.1%
 
0859816.7%
 
2660412.8%
 
543318.4%
 
341158.0%
 
434546.7%
 
832246.2%
 
731916.2%
 
631406.1%
 
930705.9%
 
-3< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n5058.1%
 
a2529.1%
 
A1011.6%
 
C11.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII51718100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11190223.0%
 
0859816.6%
 
2660412.8%
 
543318.4%
 
341158.0%
 
434546.7%
 
832246.2%
 
731916.2%
 
631406.1%
 
930705.9%
 
n500.1%
 
a25< 0.1%
 
A10< 0.1%
 
-3< 0.1%
 
C1< 0.1%
 

Pre-direction
Categorical

MISSING

Distinct4
Distinct (%)1.5%
Missing11444
Missing (%)97.8%
Memory size91.6 KiB
E
119 
W
107 
N
17 
S
16 
ValueCountFrequency (%) 
E1191.0%
 
W1070.9%
 
N170.1%
 
S160.1%
 
(Missing)1144497.8%
 
2020-12-12T15:51:05.473399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:05.517937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:05.571984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.955737845
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2288866.2%
 
a1144433.1%
 
E1190.3%
 
W1070.3%
 
N17< 0.1%
 
S16< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3433299.3%
 
Uppercase Letter2590.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2288866.7%
 
a1144433.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E11945.9%
 
W10741.3%
 
N176.6%
 
S166.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34591100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2288866.2%
 
a1144433.1%
 
E1190.3%
 
W1070.3%
 
N17< 0.1%
 
S16< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII34591100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2288866.2%
 
a1144433.1%
 
E1190.3%
 
W1070.3%
 
N17< 0.1%
 
S16< 0.1%
 

Street Name
Categorical

HIGH CARDINALITY

Distinct907
Distinct (%)7.8%
Missing17
Missing (%)0.1%
Memory size91.6 KiB
GEORGIA
 
488
WISCONSIN
 
333
VEIRS MILL
 
201
NEW HAMPSHIRE
 
192
OLD GEORGETOWN
 
186
Other values (902)
10286 
ValueCountFrequency (%) 
GEORGIA4884.2%
 
WISCONSIN3332.8%
 
VEIRS MILL2011.7%
 
NEW HAMPSHIRE1921.6%
 
OLD GEORGETOWN1861.6%
 
UNIVERSITY1701.5%
 
FREDERICK1521.3%
 
DEMOCRACY1431.2%
 
ROCKVILLE1351.2%
 
WOODMONT1341.1%
 
LITTLE SENECA1311.1%
 
EAST WEST1281.1%
 
CLARKSBURG1271.1%
 
COLESVILLE1241.1%
 
CENTURY1110.9%
 
RIVER1070.9%
 
TUCKERMAN950.8%
 
PARK POTOMAC880.8%
 
DARNESTOWN840.7%
 
TRALEE790.7%
 
EAMES770.7%
 
SYMPHONY PARK760.6%
 
CHEVY CHASE LAKE720.6%
 
OBSERVATION720.6%
 
MEDICAL CENTER720.6%
 
Other values (882)810969.3%
 
2020-12-12T15:51:05.657057image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique320 ?
Unique (%)2.7%
2020-12-12T15:51:05.739127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length9
Mean length9.376655558
Min length3

Overview of Unicode Properties

Unique unicode characters39
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E1262211.5%
 
R95518.7%
 
O80067.3%
 
A77347.0%
 
N71926.6%
 
I70086.4%
 
L69026.3%
 
S62795.7%
 
T54314.9%
 
C48704.4%
 
42833.9%
 
G36063.3%
 
D35863.3%
 
M30342.8%
 
H29482.7%
 
W28542.6%
 
K21762.0%
 
B21381.9%
 
Y20981.9%
 
U19241.8%
 
P18921.7%
 
V17741.6%
 
F13981.3%
 
X960.1%
 
J730.1%
 
Other values (14)2600.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10528695.9%
 
Space Separator42833.9%
 
Decimal Number910.1%
 
Lowercase Letter51< 0.1%
 
Dash Punctuation14< 0.1%
 
Other Punctuation10< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E1262212.0%
 
R95519.1%
 
O80067.6%
 
A77347.3%
 
N71926.8%
 
I70086.7%
 
L69026.6%
 
S62796.0%
 
T54315.2%
 
C48704.6%
 
G36063.4%
 
D35863.4%
 
M30342.9%
 
H29482.8%
 
W28542.7%
 
K21762.1%
 
B21382.0%
 
Y20982.0%
 
U19241.8%
 
P18921.8%
 
V17741.7%
 
F13981.3%
 
X960.1%
 
J730.1%
 
Z530.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
4283100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n3466.7%
 
a1733.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
14347.3%
 
33538.5%
 
666.6%
 
244.4%
 
511.1%
 
711.1%
 
911.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.990.0%
 
#110.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-14100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin10533796.0%
 
Common43984.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E1262212.0%
 
R95519.1%
 
O80067.6%
 
A77347.3%
 
N71926.8%
 
I70086.7%
 
L69026.6%
 
S62796.0%
 
T54315.2%
 
C48704.6%
 
G36063.4%
 
D35863.4%
 
M30342.9%
 
H29482.8%
 
W28542.7%
 
K21762.1%
 
B21382.0%
 
Y20982.0%
 
U19241.8%
 
P18921.8%
 
V17741.7%
 
F13981.3%
 
X960.1%
 
J730.1%
 
Z530.1%
 
Other values (3)920.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
428397.4%
 
1431.0%
 
3350.8%
 
-140.3%
 
.90.2%
 
660.1%
 
240.1%
 
#1< 0.1%
 
51< 0.1%
 
71< 0.1%
 
91< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII109735100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E1262211.5%
 
R95518.7%
 
O80067.3%
 
A77347.0%
 
N71926.6%
 
I70086.4%
 
L69026.3%
 
S62795.7%
 
T54314.9%
 
C48704.4%
 
42833.9%
 
G36063.3%
 
D35863.3%
 
M30342.8%
 
H29482.7%
 
W28542.6%
 
K21762.0%
 
B21381.9%
 
Y20981.9%
 
U19241.8%
 
P18921.7%
 
V17741.6%
 
F13981.3%
 
X960.1%
 
J730.1%
 
Other values (14)2600.2%
 

Street Suffix
Categorical

Distinct17
Distinct (%)0.1%
Missing24
Missing (%)0.2%
Memory size91.6 KiB
RD
2851 
AVE
2638 
DR
2008 
ST
819 
BLVD
717 
Other values (12)
2646 
ValueCountFrequency (%) 
RD285124.4%
 
AVE263822.5%
 
DR200817.2%
 
ST8197.0%
 
BLVD7176.1%
 
LN6095.2%
 
WAY4333.7%
 
CIR3162.7%
 
CT2762.4%
 
PKWY2231.9%
 
PIKE2141.8%
 
PL1921.6%
 
TER1721.5%
 
HWY1331.1%
 
LOOP450.4%
 
ALY200.2%
 
CTR130.1%
 
(Missing)240.2%
 
2020-12-12T15:51:05.815193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:05.884753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length2
Mean length2.525249936
Min length2

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
D557618.9%
 
R536018.1%
 
V335511.4%
 
A309110.5%
 
E302410.2%
 
L15835.4%
 
T12804.3%
 
S8192.8%
 
Y8092.7%
 
W7892.7%
 
B7172.4%
 
P6742.3%
 
N6092.1%
 
C6052.0%
 
I5301.8%
 
K4371.5%
 
H1330.5%
 
O900.3%
 
n480.2%
 
a240.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2948199.8%
 
Lowercase Letter720.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D557618.9%
 
R536018.2%
 
V335511.4%
 
A309110.5%
 
E302410.3%
 
L15835.4%
 
T12804.3%
 
S8192.8%
 
Y8092.7%
 
W7892.7%
 
B7172.4%
 
P6742.3%
 
N6092.1%
 
C6052.1%
 
I5301.8%
 
K4371.5%
 
H1330.5%
 
O900.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n4866.7%
 
a2433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin29553100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
D557618.9%
 
R536018.1%
 
V335511.4%
 
A309110.5%
 
E302410.2%
 
L15835.4%
 
T12804.3%
 
S8192.8%
 
Y8092.7%
 
W7892.7%
 
B7172.4%
 
P6742.3%
 
N6092.1%
 
C6052.0%
 
I5301.8%
 
K4371.5%
 
H1330.5%
 
O900.3%
 
n480.2%
 
a240.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII29553100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
D557618.9%
 
R536018.1%
 
V335511.4%
 
A309110.5%
 
E302410.2%
 
L15835.4%
 
T12804.3%
 
S8192.8%
 
Y8092.7%
 
W7892.7%
 
B7172.4%
 
P6742.3%
 
N6092.1%
 
C6052.0%
 
I5301.8%
 
K4371.5%
 
H1330.5%
 
O900.3%
 
n480.2%
 
a240.1%
 

Post-direction
Categorical

MISSING

Distinct3
Distinct (%)21.4%
Missing11689
Missing (%)99.9%
Memory size91.6 KiB
E
W
N
ValueCountFrequency (%) 
E70.1%
 
W60.1%
 
N1< 0.1%
 
(Missing)1168999.9%
 
2020-12-12T15:51:05.957816image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)7.1%
2020-12-12T15:51:06.003355image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:06.054899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.997607451
Min length1

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2337866.6%
 
a1168933.3%
 
E7< 0.1%
 
W6< 0.1%
 
N1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter35067> 99.9%
 
Uppercase Letter14< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2337866.7%
 
a1168933.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E750.0%
 
W642.9%
 
N17.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35081100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2337866.6%
 
a1168933.3%
 
E7< 0.1%
 
W6< 0.1%
 
N1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII35081100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2337866.6%
 
a1168933.3%
 
E7< 0.1%
 
W6< 0.1%
 
N1< 0.1%
 

City
Categorical

Distinct32
Distinct (%)0.3%
Missing22
Missing (%)0.2%
Memory size91.6 KiB
SILVER SPRING
2851 
ROCKVILLE
1758 
BETHESDA
1724 
CLARKSBURG
1261 
GERMANTOWN
1192 
Other values (27)
2895 
ValueCountFrequency (%) 
SILVER SPRING285124.4%
 
ROCKVILLE175815.0%
 
BETHESDA172414.7%
 
CLARKSBURG126110.8%
 
GERMANTOWN119210.2%
 
GAITHERSBURG5694.9%
 
POTOMAC4533.9%
 
CHEVY CHASE3983.4%
 
KENSINGTON2542.2%
 
OLNEY2281.9%
 
DAMASCUS1941.7%
 
BOYDS1711.5%
 
TAKOMA PARK1571.3%
 
MONTGOMERY VILLAGE1351.2%
 
BURTONSVILLE1151.0%
 
SANDY SPRING670.6%
 
WHEATON390.3%
 
DICKERSON320.3%
 
ASHTON190.2%
 
BROOKEVILLE180.2%
 
POOLESVILLE90.1%
 
CABIN JOHN70.1%
 
SPENCERVILLE60.1%
 
DARNESTOWN5< 0.1%
 
DERWOOD5< 0.1%
 
Other values (7)140.1%
 
(Missing)220.2%
 
2020-12-12T15:51:06.134968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T15:51:06.212034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length10
Mean length10.20550286
Min length3

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R1285610.8%
 
E116569.8%
 
S107989.0%
 
I86827.3%
 
L84627.1%
 
G70355.9%
 
N67395.6%
 
A67395.6%
 
V52994.4%
 
O52264.4%
 
T46723.9%
 
C45093.8%
 
B38723.2%
 
K36373.0%
 
36203.0%
 
P35433.0%
 
H31602.6%
 
M22671.9%
 
D22051.8%
 
U21401.8%
 
W12411.0%
 
Y10040.8%
 
n44< 0.1%
 
a22< 0.1%
 
J7< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter11574996.9%
 
Space Separator36203.0%
 
Lowercase Letter660.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R1285611.1%
 
E1165610.1%
 
S107989.3%
 
I86827.5%
 
L84627.3%
 
G70356.1%
 
N67395.8%
 
A67395.8%
 
V52994.6%
 
O52264.5%
 
T46724.0%
 
C45093.9%
 
B38723.3%
 
K36373.1%
 
P35433.1%
 
H31602.7%
 
M22672.0%
 
D22051.9%
 
U21401.8%
 
W12411.1%
 
Y10040.9%
 
J7< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3620100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n4466.7%
 
a2233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11581597.0%
 
Common36203.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R1285611.1%
 
E1165610.1%
 
S107989.3%
 
I86827.5%
 
L84627.3%
 
G70356.1%
 
N67395.8%
 
A67395.8%
 
V52994.6%
 
O52264.5%
 
T46724.0%
 
C45093.9%
 
B38723.3%
 
K36373.1%
 
P35433.1%
 
H31602.7%
 
M22672.0%
 
D22051.9%
 
U21401.8%
 
W12411.1%
 
Y10040.9%
 
n44< 0.1%
 
a22< 0.1%
 
J7< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
3620100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII119435100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R1285610.8%
 
E116569.8%
 
S107989.0%
 
I86827.3%
 
L84627.1%
 
G70355.9%
 
N67395.6%
 
A67395.6%
 
V52994.4%
 
O52264.4%
 
T46723.9%
 
C45093.8%
 
B38723.2%
 
K36373.0%
 
36203.0%
 
P35433.0%
 
H31602.6%
 
M22671.9%
 
D22051.8%
 
U21401.8%
 
W12411.0%
 
Y10040.8%
 
n44< 0.1%
 
a22< 0.1%
 
J7< 0.1%
 

State
Categorical

Distinct1
Distinct (%)< 0.1%
Missing23
Missing (%)0.2%
Memory size91.6 KiB
MD
11680 
ValueCountFrequency (%) 
MD1168099.8%
 
(Missing)230.2%
 
2020-12-12T15:51:06.281094image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T15:51:06.321129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:06.362664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.001965308
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M1168049.9%
 
D1168049.9%
 
n460.2%
 
a230.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter2336099.7%
 
Lowercase Letter690.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1168050.0%
 
D1168050.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n4666.7%
 
a2333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin23429100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M1168049.9%
 
D1168049.9%
 
n460.2%
 
a230.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII23429100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M1168049.9%
 
D1168049.9%
 
n460.2%
 
a230.1%
 

Zip Code
Real number (ℝ≥0)

SKEWED

Distinct48
Distinct (%)0.4%
Missing48
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean20863.1903
Minimum0
Maximum22222
Zeros2
Zeros (%)< 0.1%
Memory size91.6 KiB
2020-12-12T15:51:06.434226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20814
Q120850
median20871
Q320902
95-th percentile20910
Maximum22222
Range22222
Interquartile range (IQR)52

Descriptive statistics

Standard deviation275.5971811
Coefficient of variation (CV)0.01320973337
Kurtosis5636.06399
Mean20863.1903
Median Absolute Deviation (MAD)24
Skewness-74.45075304
Sum243160483
Variance75953.80624
MonotocityNot monotonic
2020-12-12T15:51:06.508790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%) 
20871124810.7%
 
20910118910.2%
 
2081410268.8%
 
208748657.4%
 
208528046.9%
 
209027216.2%
 
208175714.9%
 
208505424.6%
 
208544553.9%
 
208153963.4%
 
208553733.2%
 
209043673.1%
 
209063553.0%
 
208763372.9%
 
208792962.5%
 
208952542.2%
 
208322221.9%
 
208721941.7%
 
209121731.5%
 
208411701.5%
 
208771641.4%
 
208861351.2%
 
208661151.0%
 
209011020.9%
 
20816970.8%
 
Other values (23)4844.1%
 
ValueCountFrequency (%) 
02< 0.1%
 
207051< 0.1%
 
207831< 0.1%
 
208122< 0.1%
 
2081410268.8%
 
208153963.4%
 
20816970.8%
 
208175714.9%
 
2081870.1%
 
208322221.9%
 
ValueCountFrequency (%) 
222221< 0.1%
 
217711< 0.1%
 
209121731.5%
 
20910118910.2%
 
209063553.0%
 
20905910.8%
 
209043673.1%
 
20903480.4%
 
209027216.2%
 
209011020.9%
 

Latitude
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6013
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.45816149
Minimum0
Maximum39.330255
Zeros1985
Zeros (%)17.0%
Memory size91.6 KiB
2020-12-12T15:51:06.589359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138.987237
median39.040726
Q339.150106
95-th percentile39.2315982
Maximum39.330255
Range39.330255
Interquartile range (IQR)0.162869

Descriptive statistics

Standard deviation14.67036349
Coefficient of variation (CV)0.451977648
Kurtosis1.100749261
Mean32.45816149
Median Absolute Deviation (MAD)0.069377
Skewness-1.760779686
Sum379857.8639
Variance215.2195649
MonotocityNot monotonic
2020-12-12T15:51:06.670429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0198517.0%
 
39.0368611681.4%
 
39.2284281040.9%
 
39.024358530.5%
 
38.993102460.4%
 
38.985066450.4%
 
39.027459380.3%
 
38.992625360.3%
 
38.997153350.3%
 
38.997166260.2%
 
38.989518250.2%
 
38.990704240.2%
 
39.050272230.2%
 
38.992298220.2%
 
38.98721220.2%
 
38.990203210.2%
 
38.980091210.2%
 
39.029758200.2%
 
39.047684200.2%
 
39.012256200.2%
 
38.991808200.2%
 
38.993245180.2%
 
39.10334180.2%
 
38.982515180.2%
 
38.990688180.2%
 
Other values (5988)885775.7%
 
ValueCountFrequency (%) 
0198517.0%
 
38.949281110.1%
 
38.9517861< 0.1%
 
38.9518251< 0.1%
 
38.9524621< 0.1%
 
38.9576852< 0.1%
 
38.9582251< 0.1%
 
38.9601983< 0.1%
 
38.9608331< 0.1%
 
38.9609153< 0.1%
 
ValueCountFrequency (%) 
39.3302551< 0.1%
 
39.3264091< 0.1%
 
39.3182531< 0.1%
 
39.3141341< 0.1%
 
39.2987951< 0.1%
 
39.2980571< 0.1%
 
39.2912121< 0.1%
 
39.2910611< 0.1%
 
39.2909231< 0.1%
 
39.2908691< 0.1%
 

Longitude
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct6008
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-64.05217249
Minimum-77.489633
Maximum0
Zeros1985
Zeros (%)17.0%
Memory size91.6 KiB
2020-12-12T15:51:06.758004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-77.489633
5-th percentile-77.2795137
Q1-77.1973605
median-77.097562
Q3-77.024882
95-th percentile0
Maximum0
Range77.489633
Interquartile range (IQR)0.1724785

Descriptive statistics

Standard deviation28.94984307
Coefficient of variation (CV)-0.4519728518
Kurtosis1.100897196
Mean-64.05217249
Median Absolute Deviation (MAD)0.087021
Skewness1.760864597
Sum-749602.5747
Variance838.0934136
MonotocityNot monotonic
2020-12-12T15:51:06.839575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0198517.0%
 
-77.0548411681.4%
 
-77.286441040.9%
 
-77.145864530.5%
 
-77.026922460.4%
 
-77.094033450.4%
 
-77.101697380.3%
 
-77.026876360.3%
 
-77.028533360.3%
 
-77.028937260.2%
 
-77.027886250.2%
 
-77.098896240.2%
 
-77.113762230.2%
 
-77.097637220.2%
 
-77.028355220.2%
 
-77.09266210.2%
 
-77.029576210.2%
 
-77.111978200.2%
 
-77.108133200.2%
 
-77.056915200.2%
 
-77.025504200.2%
 
-77.087364180.2%
 
-77.092595180.2%
 
-77.029562180.2%
 
-77.09746180.2%
 
Other values (5983)885675.7%
 
ValueCountFrequency (%) 
-77.4896331< 0.1%
 
-77.483421< 0.1%
 
-77.4793531< 0.1%
 
-77.476611< 0.1%
 
-77.445024140.1%
 
-77.4217021< 0.1%
 
-77.415551< 0.1%
 
-77.4151151< 0.1%
 
-77.4131511< 0.1%
 
-77.4130121< 0.1%
 
ValueCountFrequency (%) 
0198517.0%
 
-76.9177591< 0.1%
 
-76.9179551< 0.1%
 
-76.9195121< 0.1%
 
-76.9199051< 0.1%
 
-76.921571< 0.1%
 
-76.9242311< 0.1%
 
-76.9248661< 0.1%
 
-76.9260971< 0.1%
 
-76.9263781< 0.1%
 

General Location
Categorical

HIGH CARDINALITY
MISSING

Distinct549
Distinct (%)71.7%
Missing10937
Missing (%)93.5%
Memory size91.6 KiB
HOIST ROOM
 
19
1ST FLOOR
 
17
RESIDENTIAL UNITS ONLY
 
16
2ND FLOOR
 
13
4TH FLOOR
 
12
Other values (544)
689 
ValueCountFrequency (%) 
HOIST ROOM190.2%
 
1ST FLOOR170.1%
 
RESIDENTIAL UNITS ONLY160.1%
 
2ND FLOOR130.1%
 
4TH FLOOR120.1%
 
B110.1%
 
3RD FLOOR100.1%
 
5TH FLOOR90.1%
 
UNIT A80.1%
 
A70.1%
 
6TH FLOOR70.1%
 
9TH FLOOR70.1%
 
6th Floor thru 10th Floor60.1%
 
SUITE B60.1%
 
UNIT B5< 0.1%
 
7TH FLOOR5< 0.1%
 
8TH FLOOR5< 0.1%
 
10TH FLOOR5< 0.1%
 
SUITE 3005< 0.1%
 
PARCEL E4< 0.1%
 
#1014< 0.1%
 
Wheaton Forest4< 0.1%
 
TRAVILLE GATEWAY DR AND SHADY GROVE RD4< 0.1%
 
UNIT 1014< 0.1%
 
#2014< 0.1%
 
Other values (524)5694.9%
 
(Missing)1093793.5%
 
2020-12-12T15:51:06.935657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique488 ?
Unique (%)63.7%
2020-12-12T15:51:07.027736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length129
Median length3
Mean length3.879688969
Min length1

Overview of Unicode Properties

Unique unicode characters74
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2193148.3%
 
a1097424.2%
 
16523.6%
 
O7831.7%
 
E7191.6%
 
R6391.4%
 
T6361.4%
 
A5691.3%
 
S5471.2%
 
L5391.2%
 
N5341.2%
 
I5061.1%
 
14140.9%
 
D4080.9%
 
C3160.7%
 
03110.7%
 
U3080.7%
 
F2860.6%
 
H2620.6%
 
22510.6%
 
G2070.5%
 
B1950.4%
 
P1810.4%
 
31800.4%
 
41380.3%
 
Other values (49)19184.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3349773.8%
 
Uppercase Letter806917.8%
 
Decimal Number17583.9%
 
Space Separator16523.6%
 
Other Punctuation2900.6%
 
Dash Punctuation960.2%
 
Open Punctuation19< 0.1%
 
Close Punctuation19< 0.1%
 
Control4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O7839.7%
 
E7198.9%
 
R6397.9%
 
T6367.9%
 
A5697.1%
 
S5476.8%
 
L5396.7%
 
N5346.6%
 
I5066.3%
 
D4085.1%
 
C3163.9%
 
U3083.8%
 
F2863.5%
 
H2623.2%
 
G2072.6%
 
B1952.4%
 
P1812.2%
 
M1241.5%
 
Y851.1%
 
V750.9%
 
K670.8%
 
W580.7%
 
J100.1%
 
Z80.1%
 
X4< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1652100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2193165.5%
 
a1097432.8%
 
o1020.3%
 
t720.2%
 
r680.2%
 
e580.2%
 
l510.2%
 
i440.1%
 
u330.1%
 
h300.1%
 
s270.1%
 
d230.1%
 
f170.1%
 
c16< 0.1%
 
g13< 0.1%
 
m10< 0.1%
 
b7< 0.1%
 
v7< 0.1%
 
p5< 0.1%
 
k4< 0.1%
 
y4< 0.1%
 
w1< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,10435.9%
 
/6121.0%
 
&4314.8%
 
#3311.4%
 
.269.0%
 
:62.1%
 
;51.7%
 
'51.7%
 
?31.0%
 
@20.7%
 
"20.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
141423.5%
 
031117.7%
 
225114.3%
 
318010.2%
 
41387.8%
 
51156.5%
 
61025.8%
 
81015.7%
 
9744.2%
 
7724.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-96100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(19100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)19100.0%
 

Most frequent Control characters

ValueCountFrequency (%) 
4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4156691.5%
 
Common38388.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2193152.8%
 
a1097426.4%
 
O7831.9%
 
E7191.7%
 
R6391.5%
 
T6361.5%
 
A5691.4%
 
S5471.3%
 
L5391.3%
 
N5341.3%
 
I5061.2%
 
D4081.0%
 
C3160.8%
 
U3080.7%
 
F2860.7%
 
H2620.6%
 
G2070.5%
 
B1950.5%
 
P1810.4%
 
M1240.3%
 
o1020.2%
 
Y850.2%
 
V750.2%
 
t720.2%
 
r680.2%
 
Other values (23)5001.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
165243.0%
 
141410.8%
 
03118.1%
 
22516.5%
 
31804.7%
 
41383.6%
 
51153.0%
 
,1042.7%
 
61022.7%
 
81012.6%
 
-962.5%
 
9741.9%
 
7721.9%
 
/611.6%
 
&431.1%
 
#330.9%
 
.260.7%
 
(190.5%
 
)190.5%
 
:60.2%
 
;50.1%
 
'50.1%
 
40.1%
 
?30.1%
 
@20.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII45404100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2193148.3%
 
a1097424.2%
 
16523.6%
 
O7831.7%
 
E7191.6%
 
R6391.4%
 
T6361.4%
 
A5691.3%
 
S5471.2%
 
L5391.2%
 
N5341.2%
 
I5061.1%
 
14140.9%
 
D4080.9%
 
C3160.7%
 
03110.7%
 
U3080.7%
 
F2860.6%
 
H2620.6%
 
22510.6%
 
G2070.5%
 
B1950.4%
 
P1810.4%
 
31800.4%
 
41380.3%
 
Other values (49)19184.2%
 

Location
Categorical

HIGH CARDINALITY

Distinct6155
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size91.6 KiB
POINT (0 0)
1985 
POINT (-77.054841 39.036861)
 
168
POINT (-77.28644 39.228428)
 
104
POINT (-77.145864 39.024358)
 
53
POINT (-77.026922 38.993102)
 
46
Other values (6150)
9347 
ValueCountFrequency (%) 
POINT (0 0)198517.0%
 
POINT (-77.054841 39.036861)1681.4%
 
POINT (-77.28644 39.228428)1040.9%
 
POINT (-77.145864 39.024358)530.5%
 
POINT (-77.026922 38.993102)460.4%
 
POINT (-77.094033 38.985066)450.4%
 
POINT (-77.101697 39.027459)380.3%
 
POINT (-77.028533 38.992625)360.3%
 
POINT (-77.026876 38.997153)350.3%
 
POINT (-77.028937 38.997166)260.2%
 
POINT (-77.027886 38.989518)250.2%
 
POINT (-77.098896 38.990704)240.2%
 
POINT (-77.113762 39.050272)230.2%
 
POINT (-77.097637 38.98721)220.2%
 
POINT (-77.028355 38.992298)220.2%
 
POINT (-77.029576 38.990203)210.2%
 
POINT (-77.09266 38.980091)210.2%
 
POINT (-77.111978 39.047684)200.2%
 
POINT (-77.056915 39.012256)200.2%
 
POINT (-77.108133 39.029758)200.2%
 
POINT (-77.025504 38.991808)200.2%
 
POINT (-77.087364 38.961922)180.2%
 
POINT (-77.157075 39.060245)180.2%
 
POINT (-77.096316 38.992975)180.2%
 
POINT (-77.09746 38.993245)180.2%
 
Other values (6130)885775.7%
 
2020-12-12T15:51:07.131325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5102 ?
Unique (%)43.6%
2020-12-12T15:51:07.211394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length28
Mean length24.93258139
Min length11

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
72914710.0%
 
234068.0%
 
9202837.0%
 
.194366.7%
 
3187496.4%
 
0179636.2%
 
1142454.9%
 
2139344.8%
 
8129504.4%
 
P117034.0%
 
O117034.0%
 
I117034.0%
 
N117034.0%
 
T117034.0%
 
(117034.0%
 
)117034.0%
 
6105043.6%
 
599963.4%
 
-97183.3%
 
495343.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number15730553.9%
 
Uppercase Letter5851520.1%
 
Space Separator234068.0%
 
Other Punctuation194366.7%
 
Open Punctuation117034.0%
 
Close Punctuation117034.0%
 
Dash Punctuation97183.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P1170320.0%
 
O1170320.0%
 
I1170320.0%
 
N1170320.0%
 
T1170320.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
23406100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(11703100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9718100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
72914718.5%
 
92028312.9%
 
31874911.9%
 
01796311.4%
 
1142459.1%
 
2139348.9%
 
8129508.2%
 
6105046.7%
 
599966.4%
 
495346.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.19436100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)11703100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common23327179.9%
 
Latin5851520.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P1170320.0%
 
O1170320.0%
 
I1170320.0%
 
N1170320.0%
 
T1170320.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
72914712.5%
 
2340610.0%
 
9202838.7%
 
.194368.3%
 
3187498.0%
 
0179637.7%
 
1142456.1%
 
2139346.0%
 
8129505.6%
 
(117035.0%
 
)117035.0%
 
6105044.5%
 
599964.3%
 
-97184.2%
 
495344.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII291786100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
72914710.0%
 
234068.0%
 
9202837.0%
 
.194366.7%
 
3187496.4%
 
0179636.2%
 
1142454.9%
 
2139344.8%
 
8129504.4%
 
P117034.0%
 
O117034.0%
 
I117034.0%
 
N117034.0%
 
T117034.0%
 
(117034.0%
 
)117034.0%
 
6105043.6%
 
599963.4%
 
-97183.3%
 
495343.3%
 

Council Districts
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing1985
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean2.667215476
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:07.272447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.46123364
Coefficient of variation (CV)0.5478498656
Kurtosis-1.271470259
Mean2.667215476
Median Absolute Deviation (MAD)1
Skewness0.3800509867
Sum25920
Variance2.135203751
MonotocityNot monotonic
2020-12-12T15:51:07.328495image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1274823.5%
 
2259022.1%
 
5169514.5%
 
4146212.5%
 
3122310.5%
 
(Missing)198517.0%
 
ValueCountFrequency (%) 
1274823.5%
 
2259022.1%
 
3122310.5%
 
4146212.5%
 
5169514.5%
 
ValueCountFrequency (%) 
5169514.5%
 
4146212.5%
 
3122310.5%
 
2259022.1%
 
1274823.5%
 

Councils
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing1985
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean2.667215476
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:07.391549image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.46123364
Coefficient of variation (CV)0.5478498656
Kurtosis-1.271470259
Mean2.667215476
Median Absolute Deviation (MAD)1
Skewness0.3800509867
Sum25920
Variance2.135203751
MonotocityNot monotonic
2020-12-12T15:51:07.447097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
1274823.5%
 
2259022.1%
 
5169514.5%
 
4146212.5%
 
3122310.5%
 
(Missing)198517.0%
 
ValueCountFrequency (%) 
1274823.5%
 
2259022.1%
 
3122310.5%
 
4146212.5%
 
5169514.5%
 
ValueCountFrequency (%) 
5169514.5%
 
4146212.5%
 
3122310.5%
 
2259022.1%
 
1274823.5%
 

Communities
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct58
Distinct (%)0.6%
Missing2008
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean26.83857659
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:07.522162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q112
median21
Q339
95-th percentile53
Maximum62
Range61
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.23095247
Coefficient of variation (CV)0.6047620453
Kurtosis-1.095505534
Mean26.83857659
Median Absolute Deviation (MAD)14
Skewness0.411890767
Sum260200
Variance263.4438181
MonotocityNot monotonic
2020-12-12T15:51:07.601730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50118110.1%
 
1910899.3%
 
1210739.2%
 
79378.0%
 
399277.9%
 
364974.2%
 
164293.7%
 
204083.5%
 
53352.9%
 
252382.0%
 
582151.8%
 
382091.8%
 
232021.7%
 
241991.7%
 
511761.5%
 
271481.3%
 
211391.2%
 
41281.1%
 
61050.9%
 
30910.8%
 
26890.8%
 
15870.7%
 
53860.7%
 
35730.6%
 
18680.6%
 
Other values (33)5664.8%
 
(Missing)200817.2%
 
ValueCountFrequency (%) 
12< 0.1%
 
21< 0.1%
 
32< 0.1%
 
41281.1%
 
53352.9%
 
61050.9%
 
79378.0%
 
8180.2%
 
93< 0.1%
 
10350.3%
 
ValueCountFrequency (%) 
62210.2%
 
61640.5%
 
60370.3%
 
59530.5%
 
582151.8%
 
573< 0.1%
 
564< 0.1%
 
55170.1%
 
54210.2%
 
53860.7%
 

Zip Codes
Real number (ℝ≥0)

MISSING

Distinct44
Distinct (%)0.5%
Missing1985
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean432.0318996
Minimum6
Maximum3065
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:07.683300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q136
median53
Q3110
95-th percentile3065
Maximum3065
Range3059
Interquartile range (IQR)74

Descriptive statistics

Standard deviation993.4121194
Coefficient of variation (CV)2.2993953
Kurtosis3.159196858
Mean432.0318996
Median Absolute Deviation (MAD)22
Skewness2.268898503
Sum4198486
Variance986867.639
MonotocityNot monotonic
2020-12-12T15:51:07.758365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%) 
306510759.2%
 
1118877.6%
 
607216.2%
 
396755.8%
 
446405.5%
 
364744.1%
 
174714.0%
 
714634.0%
 
1034463.8%
 
534213.6%
 
753683.1%
 
1103563.0%
 
123202.7%
 
63152.7%
 
513132.7%
 
312191.9%
 
1001951.7%
 
421771.5%
 
351731.5%
 
30621341.1%
 
281231.1%
 
251020.9%
 
47980.8%
 
8830.7%
 
107620.5%
 
Other values (19)4073.5%
 
(Missing)198517.0%
 
ValueCountFrequency (%) 
63152.7%
 
8830.7%
 
123202.7%
 
15370.3%
 
174714.0%
 
21610.5%
 
251020.9%
 
281231.1%
 
29360.3%
 
312191.9%
 
ValueCountFrequency (%) 
306510759.2%
 
30621341.1%
 
17691< 0.1%
 
16483< 0.1%
 
1141< 0.1%
 
1118877.6%
 
1103563.0%
 
107620.5%
 
1034463.8%
 
10260.1%
 

Municipalities
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.2%
Missing1985
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean1.258489401
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size91.6 KiB
2020-12-12T15:51:07.826924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum24
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5981708
Coefficient of variation (CV)1.269912006
Kurtosis57.91645904
Mean1.258489401
Median Absolute Deviation (MAD)0
Skewness7.079781422
Sum12230
Variance2.554149905
MonotocityNot monotonic
2020-12-12T15:51:07.888977image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
1943280.6%
 
111281.1%
 
6630.5%
 
9570.5%
 
12100.1%
 
2160.1%
 
34< 0.1%
 
223< 0.1%
 
53< 0.1%
 
142< 0.1%
 
72< 0.1%
 
162< 0.1%
 
232< 0.1%
 
41< 0.1%
 
241< 0.1%
 
21< 0.1%
 
101< 0.1%
 
(Missing)198517.0%
 
ValueCountFrequency (%) 
1943280.6%
 
21< 0.1%
 
34< 0.1%
 
41< 0.1%
 
53< 0.1%
 
6630.5%
 
72< 0.1%
 
9570.5%
 
101< 0.1%
 
111281.1%
 
ValueCountFrequency (%) 
241< 0.1%
 
232< 0.1%
 
223< 0.1%
 
2160.1%
 
162< 0.1%
 
142< 0.1%
 
12100.1%
 
111281.1%
 
101< 0.1%
 
9570.5%
 

Interactions

2020-12-12T15:50:55.626925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:55.716002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:55.800075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:55.887650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:55.973223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.061800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.149875image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.235950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.321523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.405095image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.486665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.564232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.646803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.727873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.809443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.891514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:56.969581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.049150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.127717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.215793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.299865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.387440image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.475016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.562591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.649666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.733738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.816810image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.900882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:57.986456image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.070528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.155101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.239674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.324747image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.410821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.493392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.575463image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.656533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.745109image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.828180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:58.916756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.004832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.094409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.182986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.270061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.356135image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.440707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.529284image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.614357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.703934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.790008image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.878084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:50:59.966160image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.052234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.138308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.223881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.306453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.386521image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.469092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.550162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.633734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.716805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.796374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.874942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:00.952508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.036581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.117150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.200722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.281792image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.365864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.448936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.528504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.608073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.685639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.766709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.845277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:01.925846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:02.005414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:02.085983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:02.167053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:02.246622image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:02.325190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T15:51:07.953033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T15:51:08.072635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T15:51:08.191738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T15:51:08.318848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-12T15:51:02.563395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:03.059822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:03.308035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T15:51:03.530227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

Application TypePermit NumberWork TypeAdded DateIssue DateFinal DateExpired DateStatusBuilding AreaDescription of WorkStreet NumberPre-directionStreet NameStreet SuffixPost-directionCityStateZip CodeLatitudeLongitudeGeneral LocationLocationCouncil DistrictsCouncilsCommunitiesZip CodesMunicipalities
0USE & OCCUPANCY PERMIT367997USE03/27/201907/18/201907/18/2019NaNFinaled114TH FLOOR / RESIDENTIAL UNITS ONLY/ FOR SHELL AND CORE U&amp;O - SEE 356350 / HOIST UNIT 3679768250NaNGEORGIAAVENaNSILVER SPRINGMD20910.038.993102-77.026922RESIDENTIAL UNITS ONLYPOINT (-77.026922 38.993102)5.05.07.03065.01.0
1USE & OCCUPANCY PERMIT370003OCCUPY07/03/201907/18/201907/18/2019NaNFinaled1RETAIL - YVONNE EXCLUSIVE DESIGN (AFRICAN CLOTHING) @ Westfield Montgomery Mall Space #2214 (No Building permit #)7101NaNDEMOCRACYBLVDNaNBETHESDAMD20817.039.024358-77.145864NaNPOINT (-77.145864 39.024358)1.01.019.0103.01.0
2USE & OCCUPANCY PERMIT370268OCCUPY07/16/201907/19/201907/19/2019NaNFinaled1Suite 75011810NaNGRAND PARKAVENaNROCKVILLEMD20852.039.049503-77.117908NaNPOINT (-77.117908 39.049503)1.01.019.060.01.0
3USE & OCCUPANCY PERMIT365016OCCUPY09/11/201805/02/201905/02/2019NaNFinaled1NaN8560NaNSECONDAVENaNSILVER SPRINGMD20910.00.0000000.000000NaNPOINT (0 0)NaNNaNNaNNaNNaN
4USE & OCCUPANCY PERMIT369771USE06/24/201907/22/201907/22/2019NaNFinaled1RESTAURANT7995NaNTUCKERMANLNNaNPOTOMACMD20854.039.041201-77.158732NaNPOINT (-77.158732 39.041201)1.01.020.053.01.0
5USE & OCCUPANCY PERMIT274226OCCUPY05/08/201209/18/201209/18/2012NaNFinaled1Existing use: residential Proposed use: professional office Square footage: 100 - REVISION TO EXISTING U &amp; O TO ADD MASSAGE THERAPY - THERAPIST: THERESA LYNN LOGAN - LMT: LICENSE: M06066 - EXP.: 13-31-2020 - HEALTH DEPT. WAIVED REQUIRED.; 07-22-2019 - HEALTH DEPT. WAIVER SUBMITTED.3430NHIGHSTNaNOLNEYMD20832.039.151110-77.068893NaNPOINT (-77.068893 39.15111)4.04.058.0100.01.0
6USE & OCCUPANCY PERMIT353768OCCUPY08/02/201612/05/201612/05/2016NaNFinaled1Existing use:&nbsp; vacant&nbsp;&nbsp; Proposed use:&nbsp; Mercantile\n\nSquare footage:&nbsp; 3,352\n\nPrimary use:&nbsp; Mercantile&nbsp; Secondary use:&nbsp; Storage (stock room)\n\nHazardous materials:&nbsp; No22705NaNCLARKSBURGRDNaNBOYDSMD20841.039.228428-77.286440NaNPOINT (-77.28644 39.228428)2.02.050.075.01.0
7USE & OCCUPANCY PERMIT352144OCCUPY04/14/201612/06/201612/06/2016NaNFinaled1KAY JEWELERS=SUITE 80022705NaNCLARKSBURGRDNaNBOYDSMD20841.039.228428-77.286440NaNPOINT (-77.28644 39.228428)2.02.050.075.01.0
8USE & OCCUPANCY PERMIT370107OCCUPY07/09/201907/22/201907/22/2019NaNFinaled1Existing Use: Residential Home:&nbsp;&nbsp; PROPOSED USE: Doctor's Office/2nd Flr Apt. (Building permit #823177/530028) PRIMARY USE: Doctor's Office = 51% SECONDARY USE: 2nd Floor Apartment = 39%12014NaNGEORGIAAVENaNSILVER SPRINGMD20902.039.051720-77.051771NaNPOINT (-77.051771 39.05172)4.04.023.017.01.0
9USE & OCCUPANCY PERMIT368918OCCUPY05/10/201905/10/2019NaNNaNIssued11 20 x 20 TentApproved for May 11 - 12, 2019 onlyNaNNaNNaNNaNNaNNaNNaNNaN0.0000000.000000Along Norfolk, Auburn, Del Ray and Cordell AvenuesPOINT (0 0)NaNNaNNaNNaNNaN

Last rows

Application TypePermit NumberWork TypeAdded DateIssue DateFinal DateExpired DateStatusBuilding AreaDescription of WorkStreet NumberPre-directionStreet NameStreet SuffixPost-directionCityStateZip CodeLatitudeLongitudeGeneral LocationLocationCouncil DistrictsCouncilsCommunitiesZip CodesMunicipalities
11693USE & OCCUPANCY PERMIT339113OCCUPY06/17/201510/19/201510/19/2015NaNFinaled1TENANT FIT OUT : GAS STATION = 60% &amp; CONVENIENCE STORE = 40% (Bldg permit #705468)12101NaNSNOWDEN FARMPKWYNaNCLARKSBURGMD20871.039.229396-77.251506NaNPOINT (-77.251506 39.229396)2.02.050.044.01.0
11694USE & OCCUPANCY PERMIT335516OCCUPY04/27/201506/01/201506/01/2015NaNFinaled1Occupancy is for&nbsp;Ages 3 months - 6 Yrs.\n\nChildren under age 2 are limited to 18 kids maximum; ages 2 and under&nbsp;are in classrooms #8, 9, 10, &amp;&nbsp;11.\n\n&nbsp;Classrooms 1 thru 7 shall only be occupied with children ages&nbsp;2 - 6 yrs.7730NaNBRADLEYBLVDNaNBETHESDAMD20817.039.011703-77.158866NaNPOINT (-77.158866 39.011703)1.01.012.0103.01.0
11695USE & OCCUPANCY PERMIT201736OCCUPY11/24/199904/27/2000NaNNaNIssued1NaN26029NaNRIDGERDNaNDAMASCUSMD20872.039.284653-77.207712NaNPOINT (-77.207712 39.284653)2.02.051.042.01.0
11696USE & OCCUPANCY PERMIT264742OCCUPY03/01/201012/28/201012/28/2010NaNFinaled1NaN11950NaNLITTLE SENECAPKWYNaNCLARKSBURGMD20871.039.231760-77.248491NaNPOINT (-77.248491 39.23176)2.02.050.044.01.0
11697USE & OCCUPANCY PERMIT367306OCCUPY02/14/201904/22/2019NaNNaNIssued1NaN25229NaNTRALEECTNaNDAMASCUSMD20872.039.270466-77.214717NaNPOINT (-77.214717 39.270466)2.02.051.042.01.0
11698USE & OCCUPANCY PERMIT262184OCCUPY06/23/200910/19/2010NaNNaNIssued1New Tenant Fit Out - AZALEA unit A026 - Bldg #1\nExisting Use: N/A Proposed Use: RESIDENTIAL\nSquare Footage: 1329\n#of Employee: 0\n#of Vehicle: 0\nPrimary Use: Residential = 100%\nNot ready for inspection\nBldg permit #51465113526NaNWATERFORD HILLSBLVDNaNGERMANTOWNMD20874.039.177587-77.277489NaNPOINT (-77.277489 39.177587)2.02.039.039.01.0
11699USE & OCCUPANCY PERMIT355167OCCUPY11/02/201604/24/201804/24/2018NaNFinaled14 STORY TOWNHOUSE22619NaNCLARKSBURGRDNaNCLARKSBURGMD20871.039.225945-77.290839NaNPOINT (-77.290839 39.225945)2.02.050.075.01.0
11700USE & OCCUPANCY PERMIT252255USE03/21/200703/10/2009NaNNaNIssued1NaN19427NaNDOVER CLIFFSCIRNaNGERMANTOWNMD20874.039.177027-77.278852NaNPOINT (-77.278852 39.177027)2.02.039.039.01.0
11701USE & OCCUPANCY PERMIT359255OCCUPY08/04/201704/18/201804/18/2018NaNFinaled1CONSTRUCTION TRAILER - @ the construction site for Washington Adventist Hospital - (Bldg permit #776507)12100NaNPLUM ORCHARDDRNaNSILVER SPRINGMD20904.039.049807-76.958291NaNPOINT (-76.958291 39.049807)5.05.021.012.01.0
11702USE & OCCUPANCY PERMIT289676OCCUPY03/12/201306/25/201506/25/2015NaNFinaled1Existing use:&nbsp; open&nbsp;&nbsp;&nbsp; Proposed use:&nbsp; Parking garage\n\nSquare footage:&nbsp;8290011570NaNOLD GEORGETOWNRDNaNROCKVILLEMD20852.039.049297-77.118580NaNPOINT (-77.11858 39.049297)1.01.019.060.01.0